Data-driven decision making rests, in part, on availability of high fidelity data. Presence of anomalies limits the use of data on an “as is” basis. Automatic anomaly detection is key to providing high fidelity data. We present a statistically rigorous method for automatic anomaly detection, which leverages correlations between multiple time series.
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